#gene,term

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('Output_Data/mean_Integrated_comparison_Late-Early.csv')
df

plt.scatter(df['Integrated mean (Early)'], df['Integrated mean (Late)'])

<matplotlib.collections.PathCollection at 0x1236f6d40>

pathway = pd.read_csv('Required_Refs/GSEA/41467_2016_BFncomms13041_MOESM340_ESM.csv')

pathway_dict = {}
for p in set(pathway.term.values):
    pathway_dict[p] = list(pathway[pathway.term == p].gene.values)
pathway_dict

{'beta-Alanine metabolism': ['UPB1',
  'ABP1',
  'AOC2',
  'AOC3',
  'CNDP1',
  'HIBCH',
  'SLC6A6'],
 'Multidrug resistance': ['ABCB4', 'ABCC1', 'ABCC3', 'ABCC4'],
 'Vitamin B6 Metabolism': ['PDXK', 'PDXP', 'PNPO'],
 'Xenobiotics Metabolism': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH7',
  'AKR1B1',
  'AKR1A1',
  'AKR1C4',
  'ALDH1A1',
  'ALDH1B1',
  'ALDH2',
  'ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'AOX1',
  'ZADH2',
  'SULT1A1',
  'SULT1A2',
  'SULT1A3',
  'AADAC',
  'AKR7A2',
  'CES2',
  'CYP2E1',
  'MAOA',
  'MAOB',
  'CYP51A1',
  'CBR1',
  'PAPSS1',
  'PAPSS2',
  'UGT1A10',
  'UGT1A8'],
 'Pterin Biosynthesis': ['GCH1', 'PCBD1', 'PTS', 'QDPR', 'SPR'],
 'Fatty Acid Biosynthesis': ['ELOVL1',
  'ELOVL2',
  'ELOVL3',
  'ELOVL4',
  'ELOVL5',
  'ELOVL6',
  'ELOVL7',
  'FADS1',
  'FADS2',
  'FADS3',
  'FADS6',
  'FASN',
  'HSD17B12',
  'PTPLA',
  'PTPLAD1',
  'PTPLAD2',
  'PTPLB',
  'SCD',
  'SCD5',
  'SLC25A1',
  'TECR',
  'TECRL',
  'ACACA',
  'ACACB',
  'MCAT',
  'AACS'],
 'Fatty Acids Oxidation, mitochondrial': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH5',
  'ADH6',
  'ADH7',
  'ALDH2',
  'ACAD10',
  'ACAD11',
  'ACAD9',
  'ACADM',
  'ACADS',
  'ACADSB',
  'ECHDC2',
  'ECHS1',
  'HADHA',
  'HADHB',
  'ACOT2',
  'ACSM1',
  'DCI',
  'ETFA',
  'ETFB',
  'ETFDH'],
 'Steroid Metabolism': ['FDPS',
  'GGPS1',
  'HMGCS1',
  'HMGCS2',
  'LSS',
  'MVD',
  'MVK',
  'NSDHL',
  'PMVK',
  'SC4MOL',
  'SC5DL',
  'SOAT1',
  'SQLE',
  'TM7SF2',
  'HSD17B12',
  'ABCC1',
  'ABCC4',
  'AKR1C1',
  'AKR1C4',
  'AKR1D1',
  'DHCR24',
  'CYP11A1',
  'CYP11B1',
  'CYP11B2',
  'CYP17A1',
  'CYP19A1',
  'CYP1B1',
  'CYP21A2',
  'CYP3A43',
  'CYP3A7',
  'EBP',
  'FDFT1',
  'HSD11B1',
  'HSD11B2',
  'HSD17B1',
  'HSD17B2',
  'HSD17B3',
  'HSD17B7',
  'HSD17B8',
  'HSD3B1',
  'HSD3B2',
  'SCP2',
  'SLC25A1',
  'SLCO1A2',
  'SLCO1B1',
  'SLCO1B3',
  'SLCO1C1',
  'SLCO3A1',
  'SLCO4A1',
  'SRD5A1',
  'SRD5A2',
  'STARD3',
  'STS',
  'SULT1A1',
  'SULT1E1',
  'SULT2A1',
  'SULT2B1',
  'UGT1A10',
  'UGT1A8',
  'UGT2B28',
  'UGT2B4',
  'UGT2B7'],
 'Eicosanoid Metabolism': ['AKR1B1',
  'AKR1A1',
  'AKR1C4',
  'CYP4F8',
  'ABCC4',
  'ALOX12',
  'ALOX12B',
  'ALOX15',
  'ALOX15B',
  'ALOX5',
  'CBR1',
  'CYP4F12',
  'CYP4F2',
  'CYP4F3',
  'FADS1',
  'HPGDS',
  'LTA4H',
  'LTC4S',
  'MGST2',
  'MGST3',
  'PTGDS',
  'PTGES',
  'PTGES2',
  'PTGIS',
  'PTGS1',
  'PTGS2',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLCO1A2',
  'SLCO1B1',
  'SLCO1B3',
  'SLCO2A1',
  'SLCO2B1',
  'SLCO3A1',
  'SLCO4A1',
  'TBXAS1'],
 'Pyrimidine Metabolism': ['ALDH6A1',
  'DUT',
  'NT5C',
  'TK1',
  'TK2',
  'UCK1',
  'UCK2',
  'UPP2',
  'AICDA',
  'CDA',
  'CTPS',
  'CTPS2',
  'DCTD',
  'DPYD',
  'DPYS',
  'DPYSL2',
  'DPYSL3',
  'SLC28A1',
  'SLC28A3',
  'SLC29A1',
  'UPB1',
  'UPP1'],
 'GABA shunt': ['ALDH9A1',
  'ABAT',
  'ADHFE1',
  'AKR7A2',
  'ALDH4A1',
  'ALDH5A1',
  'GAD1',
  'GAD2',
  'SLC36A1',
  'CNDP1'],
 'Pyrimidine Biosynthesis': ['CPS1',
  'PRPS1',
  'PRPS1L1',
  'PRPS2',
  'CAD',
  'DHODH',
  'UMPS'],
 'Transport, Lysosomal': ['ATP6V0A1',
  'ATP6V0A2',
  'ATP6V0A4',
  'ATP6V0B',
  'ATP6V0C',
  'ATP6V0D1',
  'ATP6V0D2',
  'ATP6V1A',
  'ATP6V1B1',
  'ATP6V1B2',
  'ATP6V1C1',
  'ATP6V1C2',
  'ATP6V1D',
  'ATP6V1E1',
  'ATP6V1E2',
  'ATP6V1F',
  'ATP6V1G1',
  'ATP6V1G2',
  'ATP6V1G3',
  'ATP6V1H',
  'SLC17A5',
  'SLC29A3',
  'SLC36A1',
  'TCIRG1'],
 'Vitamin D': ['CYP24A1', 'CYP27A1'],
 'Tyrosine metabolism': ['ADH4',
  'ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'ABP1',
  'AOC2',
  'AOC3',
  'COMT',
  'COMTD1',
  'DBH',
  'DCT',
  'DDC',
  'DUOX1',
  'DUOX2',
  'FAHD1',
  'FAH',
  'GOT1',
  'GOT2',
  'GSTZ1',
  'HGD',
  'HPD',
  'MAOA',
  'MAOB',
  'MOXD1',
  'PAH',
  'PNMT',
  'SLC16A10',
  'SLC16A2',
  'SLC18A1',
  'SLC18A2',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLC6A14',
  'SLC6A2',
  'SLC6A3',
  'SLC6A4',
  'SLC7A5',
  'SLC7A8',
  'SLCO1A2',
  'SLCO1B1',
  'SLCO1B3',
  'SLCO1C1',
  'SLCO4A1',
  'SULT1A1',
  'SULT1A2',
  'SULT1A3',
  'TAT',
  'TH',
  'TPO',
  'TYR',
  'TYRP1'],
 'Amino and Nucleotide Sugar Metabolism': ['AMDHD2',
  'CHIA',
  'CHIT1',
  'CMAS',
  'GALE',
  'GFPT1',
  'GFPT2',
  'GNE',
  'GNPDA1',
  'GNPDA2',
  'GNPNAT1',
  'MANSC1',
  'NAGK',
  'NANS',
  'PGM3',
  'RENBP',
  'TGDS',
  'UAP1',
  'UAP1L1',
  'UXS1'],
 'DNA Synthesis': ['DTYMK', 'NT5M', 'RRM1', 'RRM2', 'TYMS', 'SLC25A19'],
 'NAD Metabolism': ['CD38',
  'NADK',
  'NADSYN1',
  'NMNAT1',
  'NMNAT2',
  'NMNAT3',
  'NNMT',
  'NNT',
  'QPRT'],
 'Chondroitin and Heparan sulfate biosynthesis': ['B3GALT6',
  'B3GAT1',
  'B3GAT2',
  'B3GAT3',
  'B4GALT7',
  'CHPF',
  'CHPF2',
  'CHST11',
  'CHST12',
  'CHST13',
  'CHST3',
  'CHST7',
  'CHSY1',
  'CHSY3',
  'CSGALNACT1',
  'CSGALNACT2',
  'EXT1',
  'EXT2',
  'EXTL1',
  'EXTL2',
  'EXTL3',
  'GLCE',
  'HS2ST1',
  'HS3ST1',
  'HS3ST2',
  'HS3ST3A1',
  'HS3ST3B1',
  'HS3ST4',
  'HS3ST5',
  'HS3ST6',
  'HS6ST1',
  'HS6ST2',
  'HS6ST3',
  'NDST1',
  'NDST2',
  'NDST3',
  'NDST4',
  'UST',
  'XYLT1',
  'XYLT2'],
 'Phosphate Metabolism': ['PPA1', 'PPA2'],
 'Taurine and hypotaurine metabolism': ['GGT1', 'CDO1', 'CSAD', 'SLC6A6'],
 'Phenylalanine metabolism': ['ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'ABP1',
  'AOC2',
  'DDC',
  'GOT1',
  'GOT2',
  'HPD',
  'MAOA',
  'MAOB',
  'PAH',
  'SLC16A10',
  'SLC38A1',
  'SLC38A2',
  'SLC38A4',
  'SLC43A1',
  'SLC43A2',
  'SLC6A14',
  'SLC7A2',
  'SLC7A3',
  'SLC7A5',
  'SLC7A8',
  'TAT'],
 'Alanine and Aspartate Metabolism': ['GPT',
  'GPT2',
  'ACY3',
  'AGXT',
  'AGXT2',
  'AGXT2L1',
  'ASNS',
  'ASPA',
  'ASRGL1',
  'DDO',
  'GOT1',
  'GOT2',
  'SLC1A1',
  'SLC1A2',
  'SLC1A3',
  'SLC1A4',
  'SLC1A5',
  'SLC1A6',
  'SLC1A7',
  'SLC36A1',
  'SLC38A1',
  'SLC38A2',
  'SLC38A3',
  'SLC38A4',
  'SLC38A5',
  'SLC6A14',
  'SLC7A10',
  'SLC7A2',
  'SLC7A3',
  'SLC7A8',
  'SLC25A12',
  'SLC25A13'],
 'Blood Group Biosynthesis': ['ABO',
  'B3GNT2',
  'B3GNT3',
  'B3GNT5',
  'FUT1',
  'FUT3',
  'FUT9',
  'GCNT2',
  'ST3GAL3',
  'ST3GAL6',
  'ST8SIA5'],
 'Biotin Metabolism': ['BTD', 'HLCS', 'SLC16A1', 'SLC19A3', 'SLC5A6'],
 'DNA Demethylation': ['ADH5', 'ADH6', 'TET1', 'TET2', 'TET3', 'TDG'],
 'Cysteine Metabolism': ['AHCY',
  'AHCYL1',
  'AHCYL2',
  'CBS',
  'CTH',
  'GOT1',
  'GOT2',
  'LDHAL6A',
  'LDHAL6B',
  'MPST',
  'SLC1A4',
  'SLC1A5',
  'SLC3A1',
  'SLC6A14',
  'SLC7A10',
  'SLC7A11',
  'SLC7A8',
  'SLC7A9',
  'SUOX',
  'TST'],
 'Histone Methylation': ['DOT1L',
  'MLL3',
  'MLL4',
  'SETD7',
  'SETDB1',
  'SETDB2',
  'SUV39H1',
  'SUV39H2'],
 'O-Glycan Biosynthesis': ['A4GNT',
  'B3GNT6',
  'C1GALT1',
  'GALNT1',
  'GALNT10',
  'GALNT11',
  'GALNT12',
  'GALNT13',
  'GALNT14',
  'GALNT2',
  'GALNT3',
  'GALNT4',
  'GALNT5',
  'GALNT6',
  'GALNT7',
  'GALNT8',
  'GALNT9',
  'GALNTL2',
  'GCNT1',
  'GCNT3',
  'GCNT4',
  'ST3GAL1',
  'ST6GALNAC1',
  'UGDH'],
 'Fructose and Mannose Metabolism': ['MGAM',
  'FPGT',
  'FUK',
  'GMDS',
  'GMPPA',
  'GMPPB',
  'KHK',
  'MPI',
  'PMM1',
  'PMM2',
  'SLC2A5',
  'SLC2A7',
  'SLC5A10',
  'SLC5A9',
  'SORD',
  'TSTA3'],
 'DNA Methylation': ['DNMT1', 'DNMT3A', 'DNMT3B', 'DNMT3L', 'TRDMT1'],
 'Methionine Metabolism': ['AHCY',
  'AHCYL1',
  'AHCYL2',
  'ACY1',
  'AMD1',
  'GNMT',
  'DNMT1',
  'DNMT3A',
  'DNMT3B',
  'DNMT3L',
  'BHMT',
  'BHMT2',
  'MAT1A',
  'MAT2A',
  'MAT2B',
  'MTAP',
  'MTR',
  'SLC25A26',
  'SLC3A1',
  'SLC43A2',
  'SLC6A14',
  'SLC7A8',
  'SUOX'],
 'Inositol Phosphate Metabolism': ['IMPA1',
  'IMPA2',
  'IPMK',
  'ISYNA1',
  'MIOX',
  'PI4K2A',
  'SLC2A13',
  'SLC5A11',
  'SLC5A3'],
 'Valine, Leucine and Isoleucine Metabolism': ['HMGCL',
  'ALDH6A1',
  'ACAD8',
  'ACADM',
  'ACADSB',
  'ACAT1',
  'AUH',
  'BCAT1',
  'BCAT2',
  'BCKDHA',
  'BCKDHB',
  'DBT',
  'DLD',
  'HIBADH',
  'IVD',
  'MCCC1',
  'MCCC2',
  'MCEE',
  'MUT',
  'PCCA',
  'PCCB',
  'SLC16A1',
  'SLC16A7',
  'SLC16A8',
  'SLC1A5',
  'SLC5A8',
  'SLC38A1',
  'SLC38A2',
  'SLC38A4',
  'SLC43A1',
  'SLC43A2',
  'SLC6A14',
  'SLC7A2',
  'SLC7A3',
  'SLC7A5',
  'SLC7A6',
  'SLC7A7',
  'SLC7A8',
  'ALDH2'],
 'Ascorbate uptake': ['SLC2A10', 'SLC23A1', 'SLC23A2'],
 'Vitamin B12 Metabolism': ['MMAA'],
 'Tryptophan metabolism': ['ALDH2',
  'AADAT',
  'ABP1',
  'ACMSD',
  'AOC2',
  'ASMT',
  'CCBL1',
  'DDC',
  'GCDH',
  'HAAO',
  'INMT',
  'KMO',
  'KYNU',
  'MAOA',
  'MAOB',
  'SLC16A10',
  'SLC18A1',
  'SLC18A2',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLC25A21',
  'SLC6A14',
  'SLC7A5',
  'SLC7A8',
  'TDO2',
  'TPH1',
  'TPH2',
  'GOT2'],
 'Selenoamino acid metabolism': ['AHCY',
  'AHCYL1',
  'AHCYL2',
  'CBS',
  'CTH',
  'MAT1A',
  'MAT2A',
  'MAT2B',
  'PAPSS1',
  'PAPSS2',
  'SCLY',
  'SEPHS1',
  'SEPHS2'],
 'Hyaluronan Metabolism': ['GUSB', 'HAS1', 'HAS2', 'HAS3', 'HEXA', 'HEXB'],
 'Arginine and Proline Metabolism': ['PROSC',
  'ABP1',
  'ALDH18A1',
  'ALDH4A1',
  'AOC2',
  'AOC3',
  'DAO',
  'GOT1',
  'GOT2',
  'NAGS',
  'OAT',
  'PRODH',
  'PRODH2',
  'PYCR1',
  'PYCR2',
  'PYCRL',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLC25A15',
  'SLC25A2',
  'SLC36A1',
  'SLC38A1',
  'SLC38A2',
  'SLC38A4',
  'SLC5A1',
  'SLC6A14',
  'SLC6A7',
  'SLC6A8',
  'SLC7A1',
  'SLC7A2',
  'SLC7A3',
  'SLC7A5',
  'SLC7A6',
  'SLC7A7'],
 'Ubiquinone Biosynthesis': ['HMGCR', 'FDPS', 'COQ2', 'COQ3', 'COQ6', 'COQ7'],
 'Sugar Degradation': ['AKR1A1',
  'AKR1C4',
  'AKR1B1',
  'AMY1A',
  'AMY2A',
  'AMY2B',
  'ATHL1',
  'GBA3',
  'MGAM',
  'SI',
  'TREH'],
 'Galactose metabolism': ['B4GALT1',
  'B4GALT2',
  'GALE',
  'GALK1',
  'GALK2',
  'GALT',
  'GLB1',
  'LALBA',
  'LCT',
  'NEU1',
  'SI',
  'SLC2A10',
  'SLC2A2',
  'SLC2A3',
  'SLC2A4',
  'SLC2A8',
  'SLC5A1',
  'SLC5A11',
  'SLC5A9',
  'UGP2'],
 'Creatine Metabolism': ['CKB', 'CKM', 'CKMT1B', 'CKMT2', 'GAMT', 'GATM'],
 'Folate Metabolism': ['HAO1',
  'HAO2',
  'MTR',
  'ALDH1L1',
  'DHFR',
  'FOLR1',
  'FOLR3',
  'FPGS',
  'FTCD',
  'GGH',
  'MTHFD1',
  'MTHFD2',
  'MTHFR',
  'MTHFS',
  'SLC19A1',
  'SLC46A1',
  'AMT'],
 'Fatty Acid Metabolism': ['ALDH3A2',
  'CYP4F12',
  'AADAC',
  'CROT',
  'LIPC',
  'MLYCD',
  'SLC22A4',
  'SLC22A5',
  'SLC25A20',
  'SLC27A1',
  'SLC27A2',
  'SLC27A3',
  'SLC27A4',
  'SLC27A5',
  'ACSL1',
  'ACSL3',
  'ACSL4',
  'ACSL5',
  'ECH1',
  'PCCA',
  'PCCB'],
 'Lysine Metabolism': ['ALDH2',
  'ALDH7A1',
  'AADAT',
  'AASS',
  'BBOX1',
  'PIPOX',
  'SLC25A21',
  'SLC38A4',
  'SLC3A1',
  'SLC6A14',
  'SLC7A1',
  'SLC7A2',
  'SLC7A3',
  'SPCS1',
  'SPCS2',
  'SPCS3',
  'TMLHE',
  'GCDH'],
 'Sphingolipid Metabolism': ['A4GALT',
  'ARSA',
  'ASAH1',
  'B3GALNT1',
  'B3GALT4',
  'B3GALT5',
  'B3GNT3',
  'B4GALNT1',
  'CERK',
  'COL4A3BP',
  'FUT1',
  'GAL3ST1',
  'GALC',
  'GALNS',
  'GBA',
  'GBGT1',
  'GLA',
  'GLB1',
  'NAGA',
  'NEU1',
  'SGPL1',
  'SGPP1',
  'SLC33A1',
  'SMPD3',
  'SPHK2',
  'SPTLC1',
  'SPTLC2',
  'ST3GAL2',
  'ST3GAL5',
  'ST6GALNAC2',
  'ST6GALNAC3',
  'ST6GALNAC5',
  'ST6GALNAC6',
  'ST8SIA1',
  'ST8SIA5',
  'UGCG'],
 'Carbonic Acid Metabolism': ['CA1',
  'CA12',
  'CA13',
  'CA14',
  'CA2',
  'CA3',
  'CA4',
  'CA5A',
  'CA5B',
  'CA6',
  'CA7',
  'CA8',
  'CA9'],
 'Keratan sulfate biosynthesis': ['B3GNT2',
  'B3GNT3',
  'B3GNT4',
  'B3GNT6',
  'B3GNT7',
  'B3GNT8',
  'B3GNTL1',
  'B4GALT1',
  'B4GALT2',
  'B4GALT3',
  'B4GALT5',
  'CHST1',
  'CHST2',
  'CHST3',
  'CHST4',
  'CHST5',
  'CHST6',
  'ST3GAL1',
  'ST3GAL2',
  'ST3GAL3',
  'ST3GAL4'],
 'Glycerophospholipid Metabolism': ['LCAT',
  'ABCB4',
  'ACHE',
  'AGPS',
  'ALPI',
  'ALPL',
  'ALPP',
  'ALPPL2',
  'ATP10A',
  'ATP8A1',
  'CDIPT',
  'CDS1',
  'CDS2',
  'CEPT1',
  'CHAT',
  'CHKA',
  'CHKB',
  'CHPT1',
  'CLC',
  'CRLS1',
  'DGKA',
  'DGKB',
  'DGKD',
  'DGKE',
  'DGKG',
  'DGKH',
  'DGKI',
  'DGKQ',
  'DGKZ',
  'ETNK1',
  'ETNK2',
  'GK',
  'GK2',
  'GK3P',
  'GPD1',
  'LGALS13',
  'PAFAH1B1',
  'PAFAH1B2',
  'PAFAH1B3',
  'PAFAH2',
  'PCYT1A',
  'PCYT1B',
  'PCYT2',
  'PEMT',
  'PGS1',
  'PHOSPHO1',
  'PI4K2A',
  'PIP5K1A',
  'PIP5K1B',
  'PIP5K1C',
  'PISD',
  'PLA2G10',
  'PLA2G12A',
  'PLA2G1B',
  'PLA2G2A',
  'PLA2G2D',
  'PLA2G2E',
  'PLA2G2F',
  'PLA2G4A',
  'PLA2G5',
  'PLA2G6',
  'PLA2G7',
  'PLD1',
  'PLD2',
  'PTDSS1',
  'PTDSS2',
  'SLC18A3',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLC5A7'],
 'Porphyrin and Heme Metabolism': ['ALAD',
  'ALAS1',
  'ALAS2',
  'BLVRA',
  'BLVRB',
  'CPOX',
  'CYBRD1',
  'FECH',
  'FTH1',
  'FTMT',
  'HCCS',
  'HMBS',
  'HMOX1',
  'HMOX2',
  'PPOX',
  'SLC46A1',
  'SLCO1B1',
  'SLCO1B3',
  'UROD',
  'UROS'],
 'Glycine, Serine and Threonine Metabolism': ['CBS',
  'AGXT',
  'AGXT2',
  'AGXT2L1',
  'AOC3',
  'BHMT',
  'BHMT2',
  'CHDH',
  'DAO',
  'DLD',
  'DMGDH',
  'GATM',
  'GCAT',
  'GCSH',
  'GLDC',
  'GNMT',
  'MAOA',
  'MAOB',
  'PHGDH',
  'PIPOX',
  'PSAT1',
  'PSPH',
  'SARDH',
  'SDS',
  'SHMT1',
  'SHMT2',
  'SLC1A4',
  'SLC1A5',
  'SLC32A1',
  'SLC36A1',
  'SLC38A1',
  'SLC38A2',
  'SLC38A4',
  'SLC38A5',
  'SLC3A1',
  'SLC6A12',
  'SLC6A14',
  'SLC6A5',
  'SLC6A9',
  'SLC7A10',
  'SLC7A8',
  'SLC7A9',
  'THNSL1'],
 'Glyoxylate and Dicarboxylate Metabolism': ['GRHPR',
  'HAO1',
  'HAO2',
  'GLO1',
  'HAGHL'],
 'Propanoate Metabolism': ['HIBCH',
  'SLC16A1',
  'SLC16A3',
  'SLC16A7',
  'SLC16A8',
  'SLC5A12',
  'SLC5A8'],
 'Triacylglycerol Synthesis': ['AGPAT1',
  'AGPAT2',
  'AGPAT3',
  'AGPAT4',
  'AGPAT5',
  'AGPAT6',
  'DGAT1',
  'DGAT2',
  'GNPAT',
  'GPAM',
  'LIPC',
  'LIPF',
  'LIPG',
  'LPL',
  'MGLL',
  'MOGAT1',
  'MOGAT2',
  'PLA2G3',
  'PNLIP',
  'PNLIPRP1',
  'PNLIPRP2',
  'PPAP2A',
  'PPAP2B',
  'PPAP2C'],
 'Chondroitin sulfate degradation': ['ARSB',
  'GALNS',
  'GLB1',
  'GUSB',
  'HEXA',
  'HEXB',
  'IDS',
  'IDUA',
  'NEU1'],
 'N-Glycan Degradation': ['AGA',
  'ENGASE',
  'FUCA1',
  'FUCA2',
  'GALNS',
  'GLB1',
  'HEXA',
  'HEXB',
  'MAN2B1',
  'MAN2C1',
  'MANBA',
  'NEU1',
  'NEU2',
  'NEU4'],
 'Thiamine Metabolism': ['SLC19A1', 'SLC19A2', 'SLC19A3', 'THTPA', 'TPK1'],
 'Transport, Extracellular': ['SLC3A2',
  'ATP1A1',
  'ATP1A2',
  'ATP1A3',
  'ATP1A4',
  'ATP1B1',
  'ATP1B2',
  'ATP1B3',
  'ATP1B4',
  'ATP2B1',
  'ATP2B2',
  'ATP2B3',
  'ATP2B4',
  'ATP4A',
  'ATP4B',
  'RHAG',
  'RHBG',
  'SLC12A1',
  'SLC12A2',
  'SLC12A3',
  'SLC12A4',
  'SLC12A5',
  'SLC12A6',
  'SLC12A7',
  'SLC13A1',
  'SLC13A4',
  'SLC15A2',
  'SLC17A1',
  'SLC17A2',
  'SLC17A3',
  'SLC17A4',
  'SLC20A1',
  'SLC20A2',
  'SLC24A1',
  'SLC24A2',
  'SLC24A3',
  'SLC24A4',
  'SLC26A1',
  'SLC26A11',
  'SLC26A2',
  'SLC26A3',
  'SLC26A4',
  'SLC26A6',
  'SLC26A7',
  'SLC26A8',
  'SLC26A9',
  'SLC34A1',
  'SLC34A2',
  'SLC34A3',
  'SLC4A1',
  'SLC4A10',
  'SLC4A2',
  'SLC4A3',
  'SLC4A4',
  'SLC4A5',
  'SLC4A7',
  'SLC4A8',
  'SLC4A9',
  'SLC5A5',
  'SLC8A1',
  'SLC8A2',
  'SLC8A3',
  'SLC9A1',
  'SLC9A2',
  'SLC9A3',
  'SLC9A4',
  'SLC9A5'],
 'Bile Acid Biosynthesis': ['ABCB11',
  'ABCC3',
  'ACOX2',
  'AKR1D1',
  'AMACR',
  'BAAT',
  'CH25H',
  'CYP27A1',
  'CYP39A1',
  'CYP46A1',
  'CYP7A1',
  'HSD17B4',
  'HSD3B7',
  'SCP2',
  'SLC10A1',
  'SLC10A2',
  'SLC27A2',
  'SLCO1A2',
  'SLCO1B1',
  'SLCO1B3',
  'SLCO2B1',
  'SLCO4A1'],
 'Carnitine shuttle': ['CPT1A',
  'CPT1B',
  'CPT1C',
  'CPT2',
  'CRAT',
  'SLC22A4',
  'SLC22A5',
  'SLC25A20'],
 'Transport, Golgi Apparatus': ['SLC2A1',
  'SLC35A1',
  'SLC35A2',
  'SLC35A3',
  'SLC35B2',
  'SLC35B4',
  'SLC35C1',
  'SLC35D2',
  'SLC9A7',
  'SLC9A8'],
 'Glutathione Metabolism': ['GLRX',
  'GLRX2',
  'CBS',
  'CTH',
  'GSR',
  'ADH5',
  'ESD',
  'GCLC',
  'GCLM',
  'GGT1',
  'GPX1',
  'GPX2',
  'GPX3',
  'GPX4',
  'GPX5',
  'GPX6',
  'GPX7',
  'GPX8',
  'GSS',
  'PRDX1',
  'PRDX2',
  'PRDX3',
  'HAGH',
  'GSTM1'],
 'Glutamate metabolism': ['ACY1',
  'ALDH18A1',
  'ALDH4A1',
  'GLS',
  'GLS2',
  'GLUD1',
  'GLUD2',
  'GLUL',
  'GOT1',
  'GOT2',
  'GPT',
  'GPT2',
  'NAGS',
  'SLC17A6',
  'SLC17A7',
  'SLC17A8',
  'SLC1A1',
  'SLC1A2',
  'SLC1A3',
  'SLC1A5',
  'SLC1A6',
  'SLC1A7',
  'SLC25A12',
  'SLC25A13',
  'SLC25A18',
  'SLC25A22',
  'SLC32A1',
  'SLC38A1',
  'SLC38A2',
  'SLC38A3',
  'SLC38A4',
  'SLC38A5',
  'SLC3A2',
  'SLC6A1',
  'SLC6A11',
  'SLC6A12',
  'SLC6A13',
  'SLC7A11',
  'SLC7A6',
  'SLC7A7',
  'OPLAH'],
 'Citric Acid Cycle': ['ALDH1B1',
  'GOT1',
  'GOT2',
  'ACLY',
  'ACO1',
  'ACO2',
  'ALDH4A1',
  'ALDH5A1',
  'CLYBL',
  'CS',
  'DLD',
  'DLST',
  'FAHD1',
  'FAH',
  'FH',
  'HICDH',
  'IDH1',
  'IDH2',
  'IDH3A',
  'IDH3B',
  'IDH3G',
  'IREB2',
  'MDH1',
  'MDH1B',
  'MDH2',
  'OGDH',
  'PCK1',
  'PCK2',
  'PDHX',
  'SDHA',
  'SDHB',
  'SDHC',
  'SDHD',
  'SLC13A2',
  'SLC13A3',
  'SLC13A5',
  'SLC16A1',
  'SLC16A7',
  'SLC16A8',
  'SLC25A1',
  'SLC25A10',
  'SLC25A11',
  'SLC33A1',
  'SUCLA2',
  'SUCLG1',
  'SUCLG2',
  'SLC5A6',
  'ADSL',
  'ACSS1',
  'ACSS2',
  'DLAT',
  'ME1',
  'ME2',
  'ME3',
  'PC',
  'PDHA1',
  'PDHA2',
  'PDHB'],
 'Glycogen Synthesis': ['GBE1', 'GYG1', 'GYG2', 'GYS1', 'GYS2'],
 'Purine Metabolism': ['ADK',
  'ADSS',
  'ADSSL1',
  'AK1',
  'AK2',
  'AK3',
  'AK3L1',
  'AK5',
  'AK7',
  'AMPD1',
  'AMPD2',
  'AMPD3',
  'GMPR',
  'GMPR2',
  'GUK1',
  'ITPA',
  'NT5C1B',
  'NT5C2',
  'NT5E',
  'SLC2A9',
  'ADA',
  'APRT',
  'GDA',
  'HPRT1',
  'SLC28A2',
  'SLC28A3',
  'SLC29A2',
  'XDH'],
 'CYP Metabolism': ['CYP2A13',
  'CYP2A6',
  'CYP2C18',
  'CYP2C19',
  'CYP2C8',
  'CYP2C9',
  'CYP2D6',
  'CYP2E1',
  'CYP2F1',
  'CYP3A4',
  'CYP3A5',
  'CYP4A11',
  'CYB5D1',
  'CYP1A1',
  'CYP1A2',
  'CYP1B1'],
 'Ketone Bodies Metabolism': ['ACAT1',
  'ACAT2',
  'BDH1',
  'SLC16A1',
  'SLC16A3',
  'SLC16A8',
  'SLC5A12',
  'SLC5A8',
  'HMGCL',
  'HMGCLL1',
  'AACS',
  'ACSS1',
  'ACSS2',
  'ACOT12',
  'OXCT1',
  'OXCT2'],
 'Histidine Metabolism': ['ALDH2',
  'ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'ABP1',
  'AOC2',
  'AOC3',
  'CNDP1',
  'DDC',
  'FTCD',
  'HAL',
  'HDC',
  'HNMT',
  'MAOA',
  'MAOB',
  'SLC22A1',
  'SLC22A2',
  'SLC22A3',
  'SLC38A3',
  'SLC38A4',
  'SLC3A1',
  'SLC6A14',
  'UROC1'],
 'Urea Cycle': ['GATM',
  'ABP1',
  'ACY1',
  'AQP9',
  'CPS1',
  'SLC14A1',
  'SLC14A2',
  'ARG1',
  'ARG2',
  'ASL',
  'ASS1',
  'NOS1',
  'NOS3',
  'OTC'],
 'Ethanol Metabolism': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH4',
  'ADH7',
  'ALDH1A1',
  'ALDH1B1',
  'ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'AOX1',
  'ZADH2'],
 'Riboflavin Metabolism': ['ACP1', 'ACP2', 'ENPP1', 'FLAD1', 'RFK', 'TYR'],
 'Glycolysis and Gluconeogenesis': ['PFKFB1',
  'PFKFB2',
  'PFKFB3',
  'PFKFB4',
  'ACYP1',
  'ACYP2',
  'ALDOA',
  'ALDOB',
  'ALDOC',
  'BPGM',
  'ENO1',
  'ENO2',
  'ENO3',
  'FBP1',
  'FBP2',
  'G6PC',
  'G6PC2',
  'G6PC3',
  'GAPDH',
  'GAPDHS',
  'GCK',
  'GPD2',
  'GPI',
  'HK1',
  'HK2',
  'HK3',
  'HKDC1',
  'LDHA',
  'LDHAL6A',
  'LDHAL6B',
  'LDHB',
  'LDHC',
  'LDHD',
  'PCK1',
  'PCK2',
  'PFKL',
  'PFKM',
  'PFKP',
  'PGAM1',
  'PGAM2',
  'PGK1',
  'PGK2',
  'PGM1',
  'PGM2',
  'PKLR',
  'PKM2',
  'SLC16A1',
  'SLC16A3',
  'SLC16A7',
  'SLC16A8',
  'SLC25A11',
  'SLC2A1',
  'SLC2A10',
  'SLC2A11',
  'SLC2A12',
  'SLC2A14',
  'SLC2A2',
  'SLC2A3',
  'SLC2A4',
  'SLC2A6',
  'SLC2A7',
  'SLC2A8',
  'SLC2A9',
  'SLC5A1',
  'SLC5A11',
  'SLC5A12',
  'SLC5A2',
  'SLC5A3',
  'SLC5A8',
  'SLC5A9',
  'TPI1',
  'UEVLD'],
 'Keratan sulfate degradation': ['AGA',
  'FUCA1',
  'FUCA2',
  'GALNS',
  'GLB1',
  'GNS',
  'HEXA',
  'HEXB',
  'NAGA',
  'NEU1'],
 'Fatty Acids Oxidation, peroxisomal': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH5',
  'ADH6',
  'ADH7',
  'ALDH1A1',
  'ZADH2',
  'HSD17B4',
  'ACOT8',
  'ACAA1',
  'ACOX1',
  'ACOX3',
  'EHHADH',
  'PHYH'],
 'Transport, Mitochondrial': ['AQP8',
  'AQP9',
  'MMAA',
  'SLC25A14',
  'SLC25A27',
  'SLC25A3'],
 'Phosphoinositide Signalling': ['HCST',
  'INPP1',
  'INPP4A',
  'INPP4B',
  'INPP5A',
  'INPP5B',
  'INPP5D',
  'INPP5E',
  'INPPL1',
  'ITPKA',
  'ITPKB',
  'ITPKC',
  'MTMR3',
  'OCRL',
  'PIK3C2A',
  'PIK3C2B',
  'PIK3C2G',
  'PIK3C3',
  'PIK3CA',
  'PIK3CB',
  'PIK3CD',
  'PIK3CG',
  'PIK3R1',
  'PIK3R2',
  'PIK3R3',
  'PIK3R5',
  'PIP5K1A',
  'PIP5K1B',
  'PIP5K1C',
  'PLCB1',
  'PLCB2',
  'PLCB3',
  'PLCB4',
  'PLCD1',
  'PLCD3',
  'PLCD4',
  'PLCE1',
  'PLCG1',
  'PLCG2',
  'PLCH1',
  'PLCH2',
  'PLCL1',
  'PLCXD2',
  'PLCZ1',
  'PTEN',
  'SYNJ1',
  'SYNJ2'],
 'N-Glycan Biosynthesis': ['ALG1',
  'ALG10',
  'ALG12',
  'ALG2',
  'ALG3',
  'ALG5',
  'ALG6',
  'ALG8',
  'B4GALT1',
  'B4GALT2',
  'B4GALT3',
  'B4GALT5',
  'DAD1',
  'DDOST',
  'DOLPP1',
  'DPAGT1',
  'DPM1',
  'DPM2',
  'DPM3',
  'FUT8',
  'MAN1A1',
  'MAN1A2',
  'MAN1B1',
  'MAN1C1',
  'MAN2A1',
  'MAN2A2',
  'MGAT1',
  'MGAT2',
  'MGAT3',
  'MGAT4A',
  'MGAT4B',
  'MGAT5',
  'MOGS',
  'RPN1',
  'RPN2',
  'ST6GAL1',
  'TUSC3',
  'UGDH'],
 'Transport, Endoplasmic Reticular': ['SLC33A1', 'SLC35D1', 'SLC37A4'],
 'Heparan sulfate degradation': ['GNS',
  'GUSB',
  'IDS',
  'IDUA',
  'NAGLU',
  'SGSH'],
 'Oxidative Phosphorylation': ['ATP5A1',
  'ATP5B',
  'ATP5C1',
  'ATP5D',
  'ATP5E',
  'ATP5F1',
  'ATP5G1',
  'ATP5G2',
  'ATP5G3',
  'ATP5H',
  'ATP5I',
  'ATP5J',
  'ATP5J2',
  'ATP5L',
  'COX4I1',
  'COX4I2',
  'COX5A',
  'COX5B',
  'COX6A1',
  'COX6A2',
  'COX6B1',
  'COX6B2',
  'COX6C',
  'COX7A1',
  'COX7A2',
  'COX7A2L',
  'COX7B',
  'COX7B2',
  'COX7C',
  'COX8A',
  'COX8C',
  'CYC1',
  'NDUFA1',
  'NDUFA10',
  'NDUFA11',
  'NDUFA12',
  'NDUFA13',
  'NDUFA2',
  'NDUFA3',
  'NDUFA4',
  'NDUFA5',
  'NDUFA6',
  'NDUFA7',
  'NDUFA8',
  'NDUFA9',
  'NDUFAB1',
  'NDUFB1',
  'NDUFB10',
  'NDUFB2',
  'NDUFB3',
  'NDUFB4',
  'NDUFB5',
  'NDUFB6',
  'NDUFB7',
  'NDUFB8',
  'NDUFB9',
  'NDUFC1',
  'NDUFC2',
  'NDUFS1',
  'NDUFS2',
  'NDUFS3',
  'NDUFS4',
  'NDUFS5',
  'NDUFS6',
  'NDUFS7',
  'NDUFS8',
  'NDUFV1',
  'NDUFV2',
  'NDUFV3',
  'SLC25A4',
  'SLC25A5',
  'SLC25A6',
  'UCP1',
  'UCP2',
  'UCP3',
  'UQCR10',
  'UQCRB',
  'UQCRC1',
  'UQCRC2',
  'UQCRFS1',
  'UQCRH',
  'UQCRQ'],
 'CoA Biosynthesis': ['COASY',
  'PANK1',
  'PANK2',
  'PANK3',
  'PANK4',
  'PPCDC',
  'PPCS',
  'SLC25A16',
  'SLC5A6',
  'VNN1',
  'VNN2',
  'VNN3'],
 'Nucleotide Metabolism': ['ABCC4',
  'BPNT1',
  'CANT1',
  'DCK',
  'DGUOK',
  'ENTPD1',
  'ENTPD2',
  'ENTPD3',
  'ENTPD4',
  'ENTPD5',
  'ENTPD6',
  'NME1',
  'NME2',
  'NME3',
  'NME4',
  'NME6',
  'NME7',
  'NUDT2',
  'NUDT5',
  'NUDT9',
  'PAPSS1',
  'PAPSS2',
  'SLC29A1',
  'ENPP1',
  'ENPP2',
  'ENPP3'],
 'Glycogen Degradation': ['AGL', 'GAA', 'GANC', 'PYGB', 'PYGL', 'PYGM'],
 'Polyamines Metabolism': ['ADC',
  'AGMAT',
  'AMD1',
  'MAOA',
  'MAOB',
  'ODC1',
  'SAT2',
  'SMS',
  'SMOX',
  'SRM',
  'SSAT2'],
 'Cholesterol Metabolism': ['ABCA1',
  'CYP51A1',
  'DHCR7',
  'HMGCR',
  'IDI1',
  'IDI2',
  'LCAT'],
 'Pyruvate Metabolism': ['SDS',
  'GPT',
  'GPT2',
  'HAO1',
  'HAO2',
  'GRHPR',
  'AGXT',
  'AGXT2',
  'AGXT2L1'],
 'Purine Biosynthesis': ['ADSL',
  'ATIC',
  'GART',
  'PAICS',
  'PFAS',
  'PPAT',
  'GMPS',
  'IMPDH1',
  'IMPDH2',
  'PRPS1',
  'PRPS1L1',
  'PRPS2'],
 'Glycosylphosphatidylinositol (GPI)-anchor biosynthesis': ['DPM2',
  'GPAA1',
  'PGAP1',
  'PIGA',
  'PIGB',
  'PIGC',
  'PIGF',
  'PIGG',
  'PIGH',
  'PIGK',
  'PIGL',
  'PIGM',
  'PIGN',
  'PIGO',
  'PIGP',
  'PIGQ',
  'PIGS',
  'PIGT',
  'PIGV',
  'PIGW',
  'PIGX',
  'PIGZ'],
 'Pentose Phosphate Pathway': ['FBP1',
  'FBP2',
  'PGM2',
  'DCXR',
  'XYLB',
  'G6PD',
  'H6PD',
  'PGD',
  'PGLS',
  'PRPS1',
  'PRPS1L1',
  'PRPS2',
  'RBKS',
  'RPE',
  'RPIA',
  'TALDO1',
  'TKT',
  'TKTL1',
  'TKTL2'],
 'Cyclic Nucleotides Metabolism': ['ADCY1',
  'ADCY10',
  'ADCY2',
  'ADCY3',
  'ADCY4',
  'ADCY5',
  'ADCY6',
  'ADCY7',
  'ADCY8',
  'ADCY9',
  'GUCY1A2',
  'GUCY1A3',
  'GUCY1B2',
  'GUCY1B3',
  'GUCY2C',
  'GUCY2D',
  'GUCY2F',
  'NPR1',
  'NPR2',
  'PDE10A',
  'PDE11A',
  'PDE1A',
  'PDE1B',
  'PDE1C',
  'PDE2A',
  'PDE3A',
  'PDE3B',
  'PDE4A',
  'PDE4B',
  'PDE4C',
  'PDE4D',
  'PDE5A',
  'PDE6A',
  'PDE6B',
  'PDE6C',
  'PDE6D',
  'PDE6G',
  'PDE6H',
  'PDE7A',
  'PDE7B',
  'PDE8A',
  'PDE8B',
  'PDE9A'],
 'Retinol Metabolism': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH7',
  'AOX1',
  'ZADH2',
  'ALDH1A2',
  'ALDH1A3',
  'BCMO1',
  'LRAT',
  'PNPLA4',
  'RDH10',
  'RDH11',
  'RDH12',
  'RDH13',
  'RDH14',
  'RDH16',
  'RDH5',
  'RDH8',
  'SDR16C5',
  'UGT1A10',
  'UGT1A8'],
 'ROS Detoxification': ['ADH1A',
  'ADH1B',
  'ADH1C',
  'ADH7',
  'AKR1B1',
  'AKR1A1',
  'AKR1C4',
  'ALDH1A1',
  'ALDH2',
  'ALDH3A1',
  'ALDH3B1',
  'ALDH3B2',
  'AOX1',
  'ZADH2',
  'TXNRD1',
  'TXNRD2',
  'CAT',
  'EPX',
  'LPO',
  'MPO',
  'PRDX1',
  'PRDX2',
  'PRDX3',
  'PRDX5',
  'PRDX6',
  'PXDN',
  'PXDNL',
  'SOD1',
  'SOD2',
  'SOD3',
  'SLC2A10',
  'TPO'],
 'Protein Modification': ['P4HA1',
  'P4HA2',
  'P4HB',
  'GGPS1',
  'HMGCR',
  'SLC25A1',
  'SLC33A1',
  'MTMR3',
  'PLOD1',
  'PLOD2',
  'PLOD3']}

for p in pathway_dict:
    if 'OXIDATIVE' in p or 'CYCLE' in p or 'METH' in p:
        print(p)
        
for p in pathway_dict:
    if 'Oxidative' in p or 'cycle' in p or 'Meth' in p:
        print(p)

Histone Methylation
DNA Methylation
Methionine Metabolism
Oxidative Phosphorylation

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (4, 8)
plt.rcParams['svg.fonttype'] = 'none'

for k in ['Oxidative Phosphorylation', 'Methionine Metabolism', 'Citric Acid Cycle']:
    path_df = df[df.id.isin(pathway_dict[k])]
    lbls = path_df['RG2_Changes_filtered'].values
    path_df['id'] = [f'{g} {lbls[i]}' for i, g in enumerate(path_df['id'].values)]
    path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
    path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)
    x3 = path_df['id'].values
    y3_new = path_df['Integrated mean (Late)'].values
    y2_new = path_df['Integrated mean (Early)'].values
    line_colours = ['red' if p < 0.05 else 'grey' for p in path_df['Integrated pval (Late-Early)'].values]


    plt.figure()
    plt.hlines(y=x3, xmin = y3_new , xmax = y2_new, color=line_colours)
    plt.scatter(y2_new, x3, color='green', s=50, label='Early', zorder=10)
    plt.scatter(y3_new, x3, color='orange', s=50 , label='Late', zorder=10)
    plt.legend(loc="upper left")

    # # Add title and axis names
    plt.title(k, loc='left')
    plt.savefig(f'Output_Figures/Pathways/{k}.svg')
    plt.show()

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['id'] = [f'{g} {lbls[i]}' for i, g in enumerate(path_df['id'].values)]
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['id'] = [f'{g} {lbls[i]}' for i, g in enumerate(path_df['id'].values)]
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['id'] = [f'{g} {lbls[i]}' for i, g in enumerate(path_df['id'].values)]
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1865994362.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

plt.rcParams["figure.figsize"] = (3, 3)
plt.rcParams['svg.fonttype'] = 'none'

for k in [ 'Methionine Metabolism']:
    path_df = df[df.id.isin(pathway_dict[k])]
    lbls = path_df['RG2_Changes_filtered'].values
    #path_df['id'] = [f'{g} {lbls[i]}' for i, g in enumerate(path_df['id'].values)]
    path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
    path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)
    x3 = path_df['id'].values
    y3_new = path_df['Integrated mean (Late)'].values
    y2_new = path_df['Integrated mean (Early)'].values

    line_colours = ['red' if p < 0.05 else 'grey' for p in path_df['Integrated pval (Late-Early)'].values]

    plt.figure()
    plt.hlines(y=x3, xmin = y3_new , xmax = y2_new, color=line_colours, alpha=1.0)
    plt.scatter(y2_new, x3, color='green', s=50, label='Early', zorder=10)
    plt.scatter(y3_new, x3, color='orange', s=50 , label='Late', zorder=10)
    plt.legend(loc="upper left")

    # # Add title and axis names
    plt.title(k, loc='left')
    plt.savefig(f'Output_Figures/Pathways/{k}.svg')
    plt.show()

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3064542429.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3064542429.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

plt.rcParams["figure.figsize"] = (4, 6)

import matplotlib.pyplot as plt

for k in pathway_dict.keys():
    path_df = df[df.id.isin(pathway_dict[k])]

    path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
    path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)
    x3 = path_df['id'].values
    y3_new = path_df['Integrated mean (Late)'].values
    y2_new = path_df['Integrated mean (Early)'].values

    line_colours = ['red' if p < 0.05 else 'grey' for p in path_df['Integrated pval (Late-Early)'].values]

    plt.figure()
    plt.hlines(y=x3, xmin = y3_new , xmax = y2_new, color='black', alpha=0.4)
    plt.scatter(y2_new, x3, color='green', s=50, label='Early', zorder=10)
    plt.scatter(y3_new, x3, color='orange', s=50 , label='Late', zorder=10)
    plt.legend(loc="upper left")

    # # Add title and axis names
    plt.title(k, loc='left')
    plt.show()
    plt.savefig(f'Output_Figures/Pathways/{k}.svg')
    # plt.xlabel('Value of the variables')
    # plt.ylabel('Alter')

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

<Figure size 400x600 with 0 Axes>

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

<Figure size 400x600 with 0 Axes>

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

<Figure size 400x600 with 0 Axes>

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

<Figure size 400x600 with 0 Axes>

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/3627916621.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values(['RG2_Changes_filtered', 'avg'], inplace=True)

path_df = df[df.id.isin(pathway_dict['Methionine Metabolism'])]
path_df

plt.scatter(path_df['Protein-LogFC mean (Late-Early)'], path_df['RNA-LogFC mean (Late-Early)'], c=path_df['CpG-LogFC mean (Late-Early)'].values)

path_df

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (5, 10)
path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
path_df.sort_values('avg', inplace=True)
x3 = path_df['id'].values
y3_new = path_df['Integrated mean (Late)'].values
y2_new = path_df['Integrated mean (Early)'].values


plt.figure()
plt.hlines(y=x3, xmin = y3_new , xmax = y2_new, color='skyblue', alpha=0.4)
plt.scatter(y2_new, x3, color='#0096d7', s=50, label='Jazzmusiker', zorder=10)
plt.scatter(y3_new, x3, color='green', s=50 , label='Bluesmusiker', zorder=10)
plt.legend(loc="upper left")


# # Add title and axis names
# plt.title("Sterbealter der Jazz- und Bluesmusiker", loc='left')
# plt.xlabel('Value of the variables')
# plt.ylabel('Alter')

/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1321970575.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df['avg'] = (path_df['Integrated mean (Late)'].values + path_df['Integrated mean (Early)'].values)/2
/var/folders/gq/6ljhmvm1713fykdjqbl188pm0000gn/T/ipykernel_10611/1321970575.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  path_df.sort_values('avg', inplace=True)

<matplotlib.legend.Legend at 0x123801090>

plt.scatter(path_df['Integrated diff (Late-Early)'], path_df['Protein-LogFC mean (Late-Early)'], c=path_df['RNA-LogFC mean (Late-Early)'].values)

<matplotlib.collections.PathCollection at 0x124e7bc70>

plt.scatter(path_df['Integrated diff (Late-Early)'], path_df['RNA-LogFC mean (Late-Early)'], c=path_df['CpG-LogFC mean (Late-Early)'].values)

<matplotlib.collections.PathCollection at 0x1250ac700>

plt.scatter(path_df['Protein-LogFC mean (Late-Early)'], path_df['RNA-LogFC mean (Late-Early)'], c=path_df['CpG-LogFC mean (Late-Early)'].values)

<matplotlib.collections.PathCollection at 0x1250ef430>

# Now we can iterate through each one and do whatever we need to do
plt.rcParams["figure.figsize"] = (12, 3)
plt.rcParams['svg.fonttype'] = 'none' 
import os
from sciviso import Boxplot

fig_size = (12, 3)
vis_opts = {"figsize": fig_size, "title_font_size": 12, "axis_font_size": 10, 
           'palette': ['lightgrey', 'dimgrey']}

def plot_gene_logfcs(gene_name, cond0, cond1, x="Stage", cmap="Purples"):
    fig, ax = plt.subplots(nrows=1, ncols=5)
    #gene_name = ens_to_gene.get(gene)
    box_data_df = dfs['Integrated']
    #try:
    gene = gene_name
    box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
    gene_data = box_data_df[box_data_df['GeneId'] == gene]
    b = Boxplot(gene_data, x=x, y="values",  add_dots=True, 
            add_stats=True, title=f'Integrated diff.',
            xlabel="Sample", ylabel=f'diff. {cond1}-{cond0}', config=vis_opts)
    b.plot(ax=ax[0], legend=False)
    #nt('Integrated')
    
    box_data_df = dfs['Protein-LogFC']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'Protein logFC',
                xlabel="Sample", ylabel=f"Protein  {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[1])
    except:
        print('Protein logFC')
        
    box_data_df = dfs['Protein-Tumor']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'Protein Tumor',
                xlabel="Sample", ylabel=f"Protein  {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[2])
    except:
        print('Protein')
        
    box_data_df = dfs['RNA-LogFC']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'RNA logFC',
                xlabel="Sample", ylabel=f"RNA {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[3])
    except:
        print('RNA')
    box_data_df = dfs['Protein-Normal']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'Protein Normal',
                xlabel="Sample", ylabel=f"Protein {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[4])
    except:
        print('CpG')
    # ALso print the URL so that I can quickly check the ones that look good in protein atlas
    print(f'https://www.proteinatlas.org/{gene}-{gene_name}/pathology/renal+cancer/KIRC')
    fig.suptitle(f'{gene_name} ({cond1}-{cond0})', fontsize=16, fontweight='bold')
    fig.tight_layout()
    fig.subplots_adjust(top=0.8)
    if save_fig:
        plt.savefig(f'{fig_dir}boxplot_{gene_name}_{cond1}-{cond0}.svg')
    plt.show()

# Now we can iterate through each one and do whatever we need to do
plt.rcParams["figure.figsize"] = (12, 3)
plt.rcParams['svg.fonttype'] = 'none' 
import os
from sciviso import Boxplot

fig_size = (12, 3)
vis_opts = {"figsize": fig_size, "title_font_size": 12, "axis_font_size": 10, 
           'palette': ['lightgrey', 'dimgrey']}

def plot_gene_logfcs(gene_name, cond0, cond1, x="Stage", cmap="Purples"):
    fig, ax = plt.subplots(nrows=1, ncols=5)
    #gene_name = ens_to_gene.get(gene)
    box_data_df = dfs['Integrated']
    #try:
    gene = gene_name
    box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
    gene_data = box_data_df[box_data_df['GeneId'] == gene]
    b = Boxplot(gene_data, x=x, y="values",  add_dots=True, 
            add_stats=True, title=f'Integrated diff.',
            xlabel="Sample", ylabel=f'diff. {cond1}-{cond0}', config=vis_opts)
    b.plot(ax=ax[0], legend=False)
    #nt('Integrated')
    
    box_data_df = dfs['Protein-Normal']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'Protein Normal',
                xlabel="Sample", ylabel=f"Protein  {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[1])
    except:
        print('Protein logFC')
        
    box_data_df = dfs['Protein-Tumor']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'Protein Tumor',
                xlabel="Sample", ylabel=f"Protein  {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[2])
    except:
        print('Protein')
        
    box_data_df = dfs['RNA-Normal']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'RNA Normal',
                xlabel="Sample", ylabel=f"RNA {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[3])
    except:
        print('RNA')
    box_data_df = dfs['RNA-Tumor']
    try:
        box_data_df = box_data_df[box_data_df[x].isin([cond0, cond1])]
        gene_data = box_data_df[box_data_df['GeneId'] == gene]
        
        b = Boxplot(gene_data, x=x, y="values", add_dots=True, 
                add_stats=True, title=f'RNA Tumor',
                xlabel="Sample", ylabel=f"RNA {cond1}-{cond0}", config=vis_opts)
        b.plot(ax=ax[4])
    except:
        print('CpG')
    # ALso print the URL so that I can quickly check the ones that look good in protein atlas
    print(f'https://www.proteinatlas.org/{gene}-{gene_name}/pathology/renal+cancer/KIRC')
    fig.suptitle(f'{gene_name} ({cond1}-{cond0})', fontsize=16, fontweight='bold')
    fig.tight_layout()
    fig.subplots_adjust(top=0.8)
    if save_fig:
        plt.savefig(f'{fig_dir}boxplot_{gene_name}_{cond1}-{cond0}.svg')
    plt.show()

dfs = {}
files = os.listdir('Output_Data')
for f in files:
    if 'csv' in f and 'Boxplot_data_' in f:
        d = f.replace('.csv', '').replace('Boxplot_data_', '')
        dfs[d] = pd.read_csv(f'Output_Data/{f}')

df[df['id'] == 'TRIP13']

save_fig = False
for gene_name in ['FLT1', 'HIF1A', 'SLC3A1', 'BHMT', 'TRIP13', ]:
    # Do the same for old and young
    # And stage
    try:
        plot_gene_logfcs(gene_name, 'Stage I', 'Stage IV',  x='Tumor Stage')
        plot_gene_logfcs(gene_name, 'Early', 'Late',  x='Stage')
    except:
        print(gene_name)

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.889e-01 U_stat=1.410e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.922e-01 U_stat=2.095e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.453e-01 U_stat=2.150e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=5.720e-01 U_stat=1.135e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.974e-01 U_stat=1.670e+02

save_fig = False
for gene_name in ['AHCY', 'SUOX', 'FAH', 'GNMT']:
    # Do the same for old and young
    # And stage
    try:
        plot_gene_logfcs(gene_name, 'Stage I', 'Stage IV',  x='Tumor Stage')
        plot_gene_logfcs(gene_name, 'Early', 'Late',  x='Stage')
    except:
        print(gene_name)

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.220e-01 U_stat=1.330e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=6.397e-03 U_stat=2.575e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.695e-01 U_stat=1.960e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.266e-03 U_stat=2.065e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Stage I v.s. Stage IV: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.184e-01 U_stat=1.370e+02

save_fig = False
for gene_name in pathway_dict['Methionine Metabolism']:
    # Do the same for old and young
    # And stage
    try:
        #plot_gene_logfcs(gene_name, 'Stage I', 'Stage IV',  x='Tumor Stage')
        plot_gene_logfcs(gene_name, 'Early', 'Late',  x='Stage')
    except:
        print(gene_name)

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 8.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 9.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 11.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.470e-03 U_stat=7.530e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 43.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 47.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 42.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 43.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 47.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.591e-02 U_stat=9.355e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 36.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.395e-01 U_stat=7.420e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 40.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 51.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=2.422e-03 U_stat=8.480e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 51.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 55.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 42.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 46.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 50.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 44.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 52.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 51.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 58.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 45.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.667e-02 U_stat=4.470e+02

save_fig = False
for gene_name in pathway_dict['Protein Modification']:
    # Do the same for old and young
    # And stage
    try:
        #plot_gene_logfcs(gene_name, 'Stage I', 'Stage IV',  x='Tumor Stage')
        plot_gene_logfcs(gene_name, 'Early', 'Late',  x='Stage')
    except:
        print(gene_name)

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=5.509e-02 U_stat=3.940e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 7.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.753e-01 U_stat=7.375e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=7.148e-02 U_stat=5.480e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.403e-02 U_stat=8.000e+02

/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:161: UserWarning: The figure layout has changed to tight
  plt.tight_layout()
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/sciviso/boxplot.py:121: FutureWarning: Passing `palette` without assigning `hue` is deprecated.
  ax = sns.swarmplot(data=vis_df, x=x, y=y, hue_order=hue_order,
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 18.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/Users/ariane/opt/miniconda3/envs/roundround/lib/python3.10/site-packages/seaborn/categorical.py:3544: UserWarning: 13.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

p-value annotation legend:
ns: 5.00e-02 < p <= 1.00e+00
*: 1.00e-02 < p <= 5.00e-02
**: 1.00e-03 < p <= 1.00e-02
***: 1.00e-04 < p <= 1.00e-03
****: p <= 1.00e-04

Early v.s. Late: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.460e-01 U_stat=5.310e+02

	id	RG2_Changes_filtered	Integrated padj (Late-Early)	Integrated pval (Late-Early)	Integrated diff (Late-Early)	mannwhitneyu stat (Late-Early)	Integrated mean (Early)	Integrated mean (Late)	Protein-LogFC mean (Late-Early)	RNA-LogFC mean (Late-Early)	CpG-LogFC mean (Late-Early)	entrezgene_id	logFC_rna	padj_rna	beta_diff	adj.P.Val	logFC_protein	padj_protein
0	TNMD	TPDS_TMDE	0.801218	0.437609	-0.164047	483.0	0.767538	0.603491	0.000000	-0.128313	0.021239	64102.0	-1.039413	3.562803e-06	-0.338866	1.222205e-10	0.000000	0.000000e+00
1	CFTR	TPDS_TMDE	0.817284	0.452908	0.105628	603.0	0.535754	0.641382	0.000000	-0.078851	0.060186	1080.0	-1.454352	1.065293e-19	-0.334349	1.373687e-13	0.000000	0.000000e+00
2	KLHL13	TPDS_TMDE	0.626309	0.188472	0.012240	647.0	-1.331111	-1.318871	-0.181066	-0.012474	0.000000	90293.0	-1.930652	1.143208e-84	0.000000	0.000000e+00	-0.286963	4.654691e-12
3	SLC4A1	TPDS_TMDE	0.155585	0.003371	0.019807	773.0	-1.235604	-1.215798	-0.509032	0.158221	-0.063056	6521.0	-5.969063	1.544716e-117	-0.168266	6.557427e-30	1.290801	3.680266e-17
4	ABCC8	TPDS_TMDE	0.943679	0.733853	0.066696	571.0	-0.204150	-0.137454	0.000000	-0.024167	0.017609	6833.0	-1.838457	7.251890e-26	-0.305819	2.448549e-62	0.000000	0.000000e+00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5682	PCGF2	MDS_TMDE	0.569933	0.163894	0.161032	653.0	-0.402773	-0.241741	-0.159530	0.003430	-0.041164	7703.0	-1.293418	2.382459e-76	0.166096	1.813030e-22	-0.138880	5.396005e-05
5683	SRD5A2	MDS_TMDE	0.569933	0.160044	0.442163	654.0	0.222264	0.664427	0.000000	0.208751	-0.069215	6716.0	-2.556880	5.928200e-29	0.357678	2.793483e-12	0.000000	0.000000e+00
5684	VWA8-AS1	MDS_TMDE	0.370294	0.051920	0.340942	696.0	-0.215745	0.125198	0.000000	0.095329	-0.003953	100507240.0	-2.373903	2.261467e-109	0.138191	5.299332e-14	0.000000	0.000000e+00
5685	CHMP1B2P	MDS_TMDE	0.846102	0.559367	-0.104439	498.0	0.079870	-0.024569	0.000000	-0.195918	-0.027269	101060146.0	-1.762690	9.710864e-11	0.193037	3.327627e-06	0.000000	0.000000e+00
5686	PACRG-AS1	MDS_TMDE	0.608770	0.206305	0.257265	643.0	-0.189320	0.067945	0.000000	0.019734	-0.052330	0.0	-2.659329	1.278534e-65	0.101938	4.089113e-05	0.000000	0.000000e+00

Here we just want some validation plots in terms of the VAE, RNA and protein differences¶

Make the boxplots¶